Motivated by the general absence of real bug fixes at a sufficient scale,
previous learning based localization and repair approaches [1]}, [2]}, [3]}, [4]} mainly focused on training on mutants. Mutants are artificially generated (pseudo-)bugs that are introduced into a correct program via a mutation operator. For single token bugs, the mutation operator can be seen as a token replacement operator which can be inverted by a localization and repair model:
\(\mathcal {T} \xrightarrow{} \mathcal {T}^\prime \xrightarrow{} \mathcal {T}\) 
